Data Platform for Algorithmic Energy Trading

summary

My team and I delivered a cloud-native MVP in under 12 months, enabling scalable trading operations and centralized data visualization. This solution positioned the client for future algorithmic and ML-driven trading in energy markets.

goals

  • Enable control over decentralized, democratized, volatile, and flexible energy portfolios.
  • Facilitate quick reactions to energy price market and high-volume data management.
  • Fetch and transform real-time data from 10+ critical sources seamlessly.
  • Deliver a unified dashboard to consolidate and transform data, supporting 10+ energy traders with actionable insights.
High-level overview of technical solution.

key achievements

  • Delivered a production-ready MVP in under 12 months.
  • Built a scalable platform supporting the daily operations of 10+ full-time traders.
  • Integrated 10+ real-time data sources (REST, SOAP, WebSocket, FTP, AWS SNS), each processing 100k+ daily datapoints.

business impact

  • Established a robust platform as the technical foundation for future algorithmic trading in energy markets.
  • Centralized data visualization to support the daily operations of 10+ traders.
  • Positioned the company for the development of ML-driven trading signals.
Realtime data visualization dashboard used by 10+ traders daily.

technical highlights

  • Cloud-native data landing zone architecture with auto-scaling capabilities.
  • Real-time processing of streaming data from 10+ sources, handling 100k+ daily datapoints.
  • Robust data management ensuring data integrity and seamless real-time processing.

techstack

High-level architecture overview of the cloud-native data platform (Google Cloud).

client statement

TL;DR (AI summary):
Julius led the development of a cloud-native data platform for real-time algorithmic trading, showcasing expertise in Google Cloud and excellent communication skills.

“Julius led the development of our cloud-native data platform for real-time algorithmic trading in energy markets. Starting from scratch, he and his team launched a production-ready MVP in under a year. The platform integrates diverse data sources (REST, SOAP, WebSocket, FTP, AWS SNS), scales automatically for high workloads, and allows seamless expansion to new sources and users across business areas. The platform supports 10 trading experts daily. Going forward, algorithms on a Machine Learning platform will provide trading signals based on the data within the platform. Julius’s expertise in Google Cloud, particularly in Cloud Architecture, Machine Learning, and Data Engineering, combined with his excellent communication skills, makes him an invaluable asset to any project. His ability to understand complex requirements, translate them into business terms, and deliver effective solutions makes it a pleasure to collaborate with him. I strongly recommend working with Julius.”

Lukas Stein, Product Owner @ badenova AG & Co. KG

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Disclaimer:
I was responsible for this project as part of my role as Head of Machine Learning & GenAI - Google Cloud at adesso SE in Hamburg, Germany.